We observe that: (i) the scale of the local neighborhood has a significant effect on the denoising performance against different noise levels, point intensities, as well as various kinds of local details (ii) non-iteratively evolving a noisy input to its noise-free version is non-trivial (iii) both traditional geometric methods and learning-based methods often lose geometric features with denoising iterations, and (iv) most objects can be regarded as piece-wise smooth surfaces with a small number of features. The captured 3D point clouds by depth cameras and 3D scanners are often corrupted by noise, so point cloud denoising is typically required for downstream applications.
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